High-Accuracy Facial Depth Models derived from 3D Synthetic Data

Faisal Khan, Shubhajit Basak, Hossein Javidnia, M. Schukat, P. Corcoran
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引用次数: 4

Abstract

In this paper, we explore how synthetically generated 3D face models can be used to construct a high-accuracy ground truth for depth. This allows us to train the Convolutional Neural Networks (CNN) to solve facial depth estimation problems. These models provide sophisticated controls over image variations including pose, illumination, facial expressions and camera position. 2D training samples can be rendered from these models, typically in RGB format, together with depth information. Using synthetic facial animations, a dynamic facial expression or facial action data can be rendered for a sequence of image frames together with ground truth depth and additional metadata such as head pose, light direction, etc. The synthetic data is used to train a CNN-based facial depth estimation system which is validated on both synthetic and real images. Potential fields of application include 3D reconstruction, driver monitoring systems, robotic vision systems, and advanced scene understanding.
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基于三维合成数据的高精度面部深度模型
在本文中,我们探讨了如何使用合成生成的三维人脸模型来构建高精度的深度地面真值。这允许我们训练卷积神经网络(CNN)来解决面部深度估计问题。这些模型提供了对图像变化的复杂控制,包括姿势,照明,面部表情和相机位置。2D训练样本可以从这些模型中渲染出来,通常是RGB格式,以及深度信息。使用合成面部动画,动态面部表情或面部动作数据可以与地面真实深度和额外的元数据(如头部姿势,光线方向等)一起呈现为一系列图像帧。将合成数据用于训练基于cnn的人脸深度估计系统,并在合成图像和真实图像上进行了验证。潜在的应用领域包括3D重建、驾驶员监控系统、机器人视觉系统和高级场景理解。
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